{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "\n",
    "from statsmodels.formula import api\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize'] = [10,6]\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "walmart_data = pd.read_csv(\"./Walmart.csv\")\n",
    "walmart_data = walmart_data.drop(columns=['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 6435 entries, 0 to 6434\n",
      "Data columns (total 7 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Store         6435 non-null   int64  \n",
      " 1   Weekly_Sales  6435 non-null   float64\n",
      " 2   Holiday_Flag  6435 non-null   int64  \n",
      " 3   Temperature   6435 non-null   float64\n",
      " 4   Fuel_Price    6435 non-null   float64\n",
      " 5   CPI           6435 non-null   float64\n",
      " 6   Unemployment  6435 non-null   float64\n",
      "dtypes: float64(5), int64(2)\n",
      "memory usage: 352.0 KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Store</th>\n",
       "      <th>Weekly_Sales</th>\n",
       "      <th>Holiday_Flag</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Fuel_Price</th>\n",
       "      <th>CPI</th>\n",
       "      <th>Unemployment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6.435000e+03</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "      <td>6435.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.046965e+06</td>\n",
       "      <td>0.069930</td>\n",
       "      <td>60.663782</td>\n",
       "      <td>3.358607</td>\n",
       "      <td>171.578394</td>\n",
       "      <td>7.999151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>12.988182</td>\n",
       "      <td>5.643666e+05</td>\n",
       "      <td>0.255049</td>\n",
       "      <td>18.444933</td>\n",
       "      <td>0.459020</td>\n",
       "      <td>39.356712</td>\n",
       "      <td>1.875885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.099862e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2.060000</td>\n",
       "      <td>2.472000</td>\n",
       "      <td>126.064000</td>\n",
       "      <td>3.879000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>5.533501e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.460000</td>\n",
       "      <td>2.933000</td>\n",
       "      <td>131.735000</td>\n",
       "      <td>6.891000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>9.607460e+05</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.670000</td>\n",
       "      <td>3.445000</td>\n",
       "      <td>182.616521</td>\n",
       "      <td>7.874000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>1.420159e+06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>74.940000</td>\n",
       "      <td>3.735000</td>\n",
       "      <td>212.743293</td>\n",
       "      <td>8.622000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>45.000000</td>\n",
       "      <td>3.818686e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.140000</td>\n",
       "      <td>4.468000</td>\n",
       "      <td>227.232807</td>\n",
       "      <td>14.313000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Store  Weekly_Sales  Holiday_Flag  Temperature   Fuel_Price  \\\n",
       "count  6435.000000  6.435000e+03   6435.000000  6435.000000  6435.000000   \n",
       "mean     23.000000  1.046965e+06      0.069930    60.663782     3.358607   \n",
       "std      12.988182  5.643666e+05      0.255049    18.444933     0.459020   \n",
       "min       1.000000  2.099862e+05      0.000000    -2.060000     2.472000   \n",
       "25%      12.000000  5.533501e+05      0.000000    47.460000     2.933000   \n",
       "50%      23.000000  9.607460e+05      0.000000    62.670000     3.445000   \n",
       "75%      34.000000  1.420159e+06      0.000000    74.940000     3.735000   \n",
       "max      45.000000  3.818686e+06      1.000000   100.140000     4.468000   \n",
       "\n",
       "               CPI  Unemployment  \n",
       "count  6435.000000   6435.000000  \n",
       "mean    171.578394      7.999151  \n",
       "std      39.356712      1.875885  \n",
       "min     126.064000      3.879000  \n",
       "25%     131.735000      6.891000  \n",
       "50%     182.616521      7.874000  \n",
       "75%     212.743293      8.622000  \n",
       "max     227.232807     14.313000  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(walmart_data.info())\n",
    "display(walmart_data.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 3))\n",
    "sns.histplot(walmart_data['Weekly_Sales'], kde=True, color='skyblue', bins=30, edgecolor='black')\n",
    "plt.title('Distribution of Weekly Sales')\n",
    "plt.xlabel('Weekly Sales')\n",
    "plt.ylabel('Frequency')\n",
    "plt.grid(axis='y', alpha=0.75)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "sns.countplot(x='Holiday_Flag', data=walmart_data, palette='viridis')\n",
    "plt.title('Count by Holiday_Flag')\n",
    "plt.xlabel('Holiday_Flag')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "sns.countplot(x='Store', data=walmart_data, palette='viridis')\n",
    "plt.title('Count by Store')\n",
    "plt.xlabel('Store')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "缺失值统计：\n",
      "Store           0\n",
      "Weekly_Sales    0\n",
      "Holiday_Flag    0\n",
      "Temperature     0\n",
      "Fuel_Price      0\n",
      "CPI             0\n",
      "Unemployment    0\n",
      "dtype: int64\n",
      "\n",
      "填充后的缺失值统计：\n",
      "Store           0\n",
      "Weekly_Sales    0\n",
      "Holiday_Flag    0\n",
      "Temperature     0\n",
      "Fuel_Price      0\n",
      "CPI             0\n",
      "Unemployment    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# 1. 检查缺失值\n",
    "missing_values = walmart_data.isnull().sum()\n",
    "print(\"缺失值统计：\")\n",
    "print(missing_values)\n",
    "\n",
    "# 2. 按列的均值填充缺失值\n",
    "walmart_data_filled = walmart_data.fillna(walmart_data.mean())\n",
    "\n",
    "# 3. 检查填充后的缺失值\n",
    "missing_values_filled = walmart_data_filled.isnull().sum()\n",
    "print(\"\\n填充后的缺失值统计：\")\n",
    "print(missing_values_filled)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "#去除离群值\n",
    "\n",
    "for i in range(len(walmart_data.columns)):\n",
    "    column_name = walmart_data.columns[i]\n",
    "    mean = walmart_data[column_name].mean()\n",
    "    std = walmart_data[column_name].std()\n",
    "\n",
    "    # 定义离群值的范围\n",
    "    lower_bound = mean - 3 * std\n",
    "    upper_bound = mean + 3 * std\n",
    "\n",
    "    # 去除离群值\n",
    "    walmart_data = walmart_data[(walmart_data[column_name] >= lower_bound) & (walmart_data[column_name] <= upper_bound)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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CAtTmY141QY55CSGEsDgy8hJCCAtQndOGlkCSlxBCWACZNlST5CWEEBZAYyXJ658keQkhhAXQSvJSkQUbQgghLI6MvIQQwgJotDLy+idJXkIIYQE0VjJR9k+SvIQQwgLIMS81SV5CCGEBZNpQTZKXEEJYABl5qckkqhBCCIsjIy8hhLAAcpKymiQvIYSwABqtTJT9kyQvIYSwALJgQ02SlxBCWABZsKEmyUsIISyAjLzU6lTycu/WnpAXxuDStgW2fl7se2gCSWu23LzOnR0Jm/EKjmGNybuYwOmoz7n09SpVTODTwwmZMga9Tz0yDp3g6OTppO89XJVdqbD+nWzo3NwaO72GuAQjK7YZSE4v/ee8Abq11NGrrQ4new2Xk038uMPAhSQTAPZ6uKeTDaENrHF10pCdq3D4bCHrdueTl18dPbq5do00dG6mwdEWktJg034Tl1NLjw8NgB4ttbg6QGombP3LxJmE4vvv66ShdbD62MKZBIXlv5mqpgO3afTQAO7r44WjvTVHYjOY+UUc8Yl5pcYPf8CPOzt50MDfDkO+iaOxmcz/73kuXi6uc18fL/p096RxsAMO9tbcN2IPWTnG6ujOLW1b/x2bVi8hPS2F+kFNeHTsVIIbtygx9vfNK4ne/guXL5wGoEHDZjzw2DM3xCdcOsuPX8/m5LEDmIyF+NYPYfzLM/Co51vl/REVV6eOAFo52JNxKJYjz75dpni7oPp0WDOflO1/srP9IOLmLKHl/HfwvLubOcZ3SH+afRTBqXfmsrPjA2QeOkGntQuwqedeVd2osN5tddzZWseKbQY++T6X/AIYP8gOa6vS69zR2JrB3W3YsCefGctziE82Mf5+Oxztir7lOTtocXHQ8tNOAx8szWHZrwZCG1gzrLdtNfWqdM0CNPS5Q8PvRxQWbDRxJU1hWE8t9vqS4/094IFwLX+dVfhqo4mT8QpDummp56KOO3NZYdZqo3lbvat2Jq5HB/vx0L0+zPziLE+/ephcg4mP3miGzU1+tLBNmAurNyQyIeIwL047hpWVho/eCMNWX/yvwFavZc/BNJaujK+ObpTZ3p0bWbHoY+57ZByvz1hGQFATZk+bQEZayd9WYo/so2O3e3hh2pdMjVqCu4cPs95+mmspV8wxVxIv8uGr/8GnfjAvTvuSNz/5ngFDnkSnK+VNVIM0Wm2Ft7qoTvXq6sYdnIycRdJPv5YpPvCpYeTGXeL4yx+QdeIs5z9bSuKPGwl+bpQ5JnjyaC4u+J5LS1aSdfwMhydEYszJI2DUQ1XUi4q7s42OTXvzORJnJCHFxNLNebg4aGgZUvoAu2cbHdFHC9hzvJCkaworthnIL1ToFFZUJzHVxKL1eRw9ZyQlQ+HUJSNrdxtoEWxFTc9idArVEHNG4VCcQnIGrNurUFgIrUNKbljHphrOJMDuEwopGfDbYYXEa9C+sTq+0ATZecVbXkF19Kb8Hh7gyzc/XuKPvdc4ez6HqDmn8XSzoVvH0r9YvfzucTZsv8q5S7mcOZ/D+3NP41NPT5MQB3PMD2sTWbb6MsdOZVVHN8ps88//pdvdD9K19yD8Ahry2LjXsNHb8sfW1SXGj33+PXr2f4SA4Kb41g9mxIQ3URSFE4f+NMesXvopLdp14+ERk2kQEoqXTwBtOvbE2bX2fTnVaDUV3uqiOpW8ysu1cxuSt0aryq5u3olb5zYAaHQ6XNo2J3nLruIARSF56y5cO99RjS29NQ9nDS4OWk5eLJ7eycuH80kmgnxKfpmttFDfS11HAU5eNBLkU/pwzc5GQ14+mG4+G1mltFrwdYO4JHUj4pIU6nuU/GH199DcEH82UcH/uvhAL5g8WMv4e7Xc006DnU3ltr0y+Hrp8XCzYf+hdHNZdo6RY6eyCGviVOb9ONoXfUnJzCqs9DZWpsKCAi6cOU6zVp3MZVqtlmatOnE29lCZ9pGfn4fRWIiDU9FQ22QycXj/Trx9GzBr2gReGNWL96Y+wcE/t1VJH26X1kpT4a0uKvcxr+TkZBYuXEh0dDSJiYkA+Pj40KVLF0aNGkW9evUqvZFVRe/tiSEpWVVmSEpG5+KE1laPzs0FrbU1hisp18Wk4NA0pDqbektO9kVv0Mwc9T/nzBwTzg4lv3kd7DRYaTUl1FHwdis54TnYQt8ONuw6UrPDEXsb0Go1ZF93eCc7DzycS67jaEuJ8Q52xbfPJkDsRRNp2eDmCD1baRnWQ8PiX00oNZisr+fupgMgNU39OlxLz8fdVVemfWg0MGl0EIePZxB3MbfS21iZsjKvYTIZbxgRObl6kBB/rkz7+PHr2bi41TMnwMz0VAx5OWxYtYhBwyfy0BPPceTgH8z78AWmTPuCps3bV3Y3bktdHUFVVLmS1969e+nXrx/29vb06dOHJk2aAJCUlMT//d//8f7777Nx40bat7/5i24wGDAYDKoyvb72zTHXZu2aWPPIXcXP2Rc/V/0/H70OnhpoR9I1Exv21ILVGlXg2IXiDHU1Ha6kmZg40IpALziXVHPt6tPdkxeeKv7C9ErUidve5+SxwQQH2PHM60dve1+13fqVC9n7x0ZenPYlOpuiz42iFB3LbNOxJ3cPfByAgOCmnDnxFzs2/lALk9e/eqLsBuVKXs888wxDhgxh3rx5aDTqbwGKojB+/HieeeYZoqOjS9lDkaioKN5+W72oIjIykg7laUwlMCQlo/f2VJXpvT0pSM/ElGcgP/kapsJC9F4e18V4YEhUj9iq25G4Qs4nFU/3Wf9vasDJXkPGP0ZSTvZa4q+WvFIsO1fBaFLMo7biOup9QFHiGj/IjrwChQVr8zDV8BqGnHwwmRQcrls34mAL2aXk8aw8yhUPkJYN2XkKbo4aziXV3NDrj72pHP/HMSidddFr5u6qU42+3FxsOH0u+5b7e25MMOHt3Hj2zaNcTa39X0QcndzQaq1uWJyRmZaCi6tHKbWKbFr9NRtWLuL5t+ZRP6iJep9W1vjWV8+i+NYP4fTxg5XXeFElypXK//rrL55//vkbEheARqPh+eefJyYm5pb7iYiIID09XbVFRESUpymVIm13DB69OqvKPHt34druGACUggLSDxzFs1d4cYBGg8dd4aTtrtk3t6EAktMV85aYaiI920TjgOJjVXodBHprOZdYcqYxmuDSFRON6xfX0QBNAqw4l1ic8PQ6eHqQHUYjfPVLHoW1YNW0yQQJ1yDIW/1eDPLWcCml5CQTn6IQfF18sI+G+FLiAZzsik4XyMqt2TnD3DwT8Yl55u3cpVxSruXTtmXxUkl7OyvCGjty7GTmTff13JhgunV05/m3jpF4xXDT2NrCWqejQcNmqsUWJpOJ44f2ENK0Van1NqxazC8/fMlzb8wlqFHzG/YZ1CiMxMvnVeVJl8/j4VX7lsnLgg21ciUvHx8f9uzZU+r9e/bswdvb+5b70ev1ODs7q7bKmDa0crDHuXUozq1DAbAPro9z61BsA4reiE3fmULrRR+Y489/sRz74ABCo17CoWkIgeOH4zukP3GzF5tj4mYtImDMI/g/MRjH0BBazH0Lawc7Li5ZedvtrWw7Ygro296G5sFW+HpoebyvLenZRedl/W3CYFu6tSo+JrI9poDw5jo6hFrj7aZhyF16bKw1/HmsqI5eB08PtsNGB99uycPWRoOTfdFWwneYavXnCYU7GmpoGaTBwxn6t9egs4ZDZ4sSzcBOGnq2Km7knliFEF/o1FSDhxN0b6HB1w32nSqK11lDr9Ya/DzAxQGCvGFIdy2pmXA2sUa6eFM/rE3giYfq06W9G8EN7Hn1mUYkX8tn557i0cnHkWE8cI+P+fbkscHcfacn78w+RW6eEXdXHe6uOmxsiv8VuLvqaBRkj79P0TA1ONCeRkH2ODnW7Gmhdw98nN9/XcWubWtIuHSWpfPfI9+QS9degwBYOPt1Vv73/8zxG1YuYs23nzFyYiQeXn6kX0sm/Voyebk55ph+g0ay74+N/L55JVcSLrB13XIO7dtBj3seqfb+3YokL7VyvRtffPFFnnrqKfbv30/v3r3NiSopKYktW7bw5ZdfMmPGjCppaFm4tGtB+JZvzLfDZrwKwMWvV3JoTAR633rYBRR/o8o9d4m9948j7OMIgp4ZQd6lRA6Pe53kzTvNMQkr1mNTz50mkc8WnaT813H23DeW/OsWcdQGWw4UYKPTMPQuPXZ6DWcTjMxfk6saKXm6aHG0LR6JHTxViIOdhv6dbHB20BB/1cT8NbnmkUaAl5V55eEbIx1UjzdtcTapmTU3Ijl+sWjasEdLDQ62GpLSYPl2E9n/G0y4OGhQKG5ffAqsjjbRs6WWnq00pGbCip0mrv5vwZ6igJerhlbBGmx1kJkHcYkKvx1SMNbCU72+XX0ZW70VL44LwdHBmsMnMnj5nePkFxT32d9bj4tz8cd88P8S2exp6lHI+5+eZsP2qwDc39ebUY8EmO+bM73FDTE1oUO3fmRmXGPNt5+TkZZC/eCmPPvGXJz/N22YmpyoOi7028YVFBYWMP+jl1T7ue+Rcdw/bDwAd3TuxWPjXmPDyoUsX/Ah3n6BjH/5Ixo3q12riUEWbFxPoyjlW0P13Xff8cknn7B//36MxqL/ilZWVrRr144pU6bwyCMV/8ayVte0wnUt1YCCWCbPqV3n01SHWc848u7yWjD/WM1eG2ZFz4dvfky4Ltr+Qzi/Hc25dWAd06O5faXt69Rj91a4buOl6yqtHbVFuecBhg4dytChQykoKCA5uWjRgqenJzpd2ZbnCiGEKL+6er5WRVV4Elun0+HrW/sOagohhKj76tSFeYUQoq6SY15qkryEEMICyEnKapK8hBDCAsjIS02SlxBCWABJXmqSvIQQwgLItKGaPBtCCCEsjoy8hBDCAsi0oZokLyGEsAAybagmz4YQQlgCjabiWwXMnTuXoKAgbG1t6dSp000vyg4wa9YsmjZtip2dHQEBATz//PPk5eXdtM7tkJGXEEJYgOqcNvzuu++YMmUK8+bNo1OnTsyaNYt+/foRGxuLl5fXDfHLli3jlVdeYeHChXTp0oWTJ08yatQoNBoNM2fOrJI2yshLCCEsgEarrfBWXjNnzuTJJ59k9OjRhIWFMW/ePOzt7Vm4cGGJ8bt27aJr164MHz6coKAg+vbty6OPPnrL0drtkOQlhBB1nMFgICMjQ7UZDCX/EGl+fj779++nT58+5jKtVkufPn2Iji75FxG6dOnC/v37zcnq7NmzrFu3jnvvrfiV8G9FkpcQQliA2/kxyqioKFxcXFRbVFRUiY+TnJyM0Wi84YeFvb29SUws+VdZhw8fzrRp0+jWrRs6nY6GDRvSs2dPXn311Up/Hv4myUsIISzA7UwbRkREkJ6ertoiIiIqrW3bt2/nvffe47PPPuPAgQOsXLmStWvXMn369Ep7jOvJgg0hhLAAt7NgQ6/Xo9fryxTr6emJlZUVSUlJqvKkpCR8fHxKrPPGG2/wxBNPMHbsWABatmxJdnY2Tz31FK+99hraKljmLyMvIYSwALczbVgeNjY2tGvXji1btpjLTCYTW7ZsITw8vMQ6OTk5NyQoKysrABRFKWdPy0ZGXkIIYQmq8STlKVOmMHLkSNq3b0/Hjh2ZNWsW2dnZjB49GoARI0bg7+9vPm42cOBAZs6cyR133EGnTp04ffo0b7zxBgMHDjQnscomyUsIIYTK0KFDuXr1Km+++SaJiYm0adOGDRs2mBdxXLhwQTXSev3119FoNLz++uvEx8dTr149Bg4cyLvvvltlbZTkJYQQFkBTwStlVNSkSZOYNGlSifdt375dddva2prIyEgiIyOroWX/e8xqeyQhhBAVJtc2VJPkJYQQFkCuKq8myUsIISyBjLxUJHkJIYQFkJGXmqRyIYQQFkejVNUZZEIIISrNtXefrnBdt9c+r8SW1A61atpw8pysmm5CtZv1jCNrdU1ruhnVbkBBLF9tuXVcXTO2N6zZZ6zpZlS7+9tbMfOnf9/35CmDKnGqT6YNVWpV8hJCCFEyWSqvJslLCCEsgCzYUJPkJYQQlkAjI69/kmdDCCGExZGRlxBCWACZNlST5CWEEJZAFmyoSPISQggLUN1Xla/tJHkJIYQlkJGXiiQvIYSwAHLMS01SuRBCCIsjIy8hhLAEcp6XiiQvIYSwBDJtqCLJSwghLIBGRl4qkryEEMISyMhLRZKXEEJYALmqvJo8G0IIISyOjLyEEMISyBU2VCR5CSGEJZBpQxVJXkIIYQlk5KUiyUsIISyALNhQk+QlhBCWQM7zUpFnQwghhMWRkZcQQlgCOUlZpc4mr/6dbOjc3Bo7vYa4BCMrthlITlduWqdbSx292upwstdwOdnEjzsMXEgyAWCvh3s62RDawBpXJw3ZuQqHzxaybnc+efnV0aPSuXdrT8gLY3Bp2wJbPy/2PTSBpDVbbl7nzo6EzXgFx7DG5F1M4HTU51z6epUqJvDp4YRMGYPepx4Zh05wdPJ00vcersqulNuB35ayd/MCsjOu4lU/lN6PvIFvUKsSY5Mvn2LnL/9H0oWjZKTGc9fDEbTvNUoVc3DHMmJ2fEtGajwAHr6N6XLvBEKa96jqrpTLH5uW8dvahWSmJ+PboCmDR75Gg4Yl9/vPrSvYv/MnEi+eBsA/OIz+Qyer4hVFYdOPn/LnthXkZmcS1OQOHvzPm9TzCaqO7pTZkV1L+eu3BeRmJuPhG0rXQa/j1aDkfgOcObSBfRtnk3ktHhfPQDr1f5EGzYpfywJDNn+u/5hzR7eQl52Gk3t9WnZ9grDwYdXRnXKRy0Op1clno3dbHXe21rFim4FPvs8lvwDGD7LD2qr0Onc0tmZwdxs27MlnxvIc4pNNjL/fDke7om87zg5aXBy0/LTTwAdLc1j2q4HQBtYM621bTb0qnZWDPRmHYjny7NtlircLqk+HNfNJ2f4nO9sPIm7OElrOfwfPu7uZY3yH9KfZRxGcemcuOzs+QOahE3RauwCbeu5V1Y1yO7FvHdt/jKLLgImMiFhFPf9QVswZQ3ZmSonxBfm5uHrW587BL+DgXK/EGCdXH3oMfpERr6zkiak/EtikM6vmTST58qmq7Eq5xESv5+elH3D3gxOY/M4P+DUI5av3nyIrveR+nzm+hzbhAxj32iImvb0MVw8fvnz/SdJTk8wx239ZwM6N/+XB0ZE8M205Nno7vnr/KQryDdXVrVs6HbOO6J/fp12fiTz03ErcfZuydsFYcrNK7nfiuQNsWfYCTTs8zEPPrSKoeR82fj2J1MST5phdP7/Pxdid9Br2IUNfXEvLbiPY+dN0zh3dWl3dKjutpuJbHVQnk9edbXRs2pvPkTgjCSkmlm7Ow8VBQ8uQ0geaPdvoiD5awJ7jhSRdU1ixzUB+oUKnsKI6iakmFq3P4+g5IykZCqcuGVm720CLYKsaf29c3biDk5GzSPrp1zLFBz41jNy4Sxx/+QOyTpzl/GdLSfxxI8HPjTLHBE8ezcUF33NpyUqyjp/h8IRIjDl5BIx6qIp6UX77ti6iVddHaBn+EJ6+jej76NvobGw5suvHEuN9g1rR88GpNGs/ACtrmxJjGrXqRUiLHrh5BeHuHUz3Qc9jo7fnclxMFfakfHasX0ynu4bQoceDeNdvxIP/iUSnt2XPbytLjB8+8SO63P0o/kHN8PILYciT01FMJk4d3Q0Ujbp+3/A1vQePo0X73vg1aMqwp98nI+0KR/fffARfnQ7/vphmnYYQ2uEh3LwbceeDb2Ots+XE3pJf78M7vyGgSTfa9ByDm3dDOvR7Dk//MI78sdQck3Q+hibtBuPXsBNO7vUJ6zwUD9+mXLl4qLq6VXYabcW3OqjO9crDWYOLg5aTF43msrx8OJ9kIsin5O5aaaG+l7qOApy8aCTIp/Thmp2Nhrx8MN18NrLWce3chuSt0aqyq5t34ta5DQAanQ6Xts1J3rKrOEBRSN66C9fOd1RjS0tnLMwn8cJRApt2MZdptFoCQ7twOe5gpTyGyWTk+L61FOTn4BdSO/pdWJhPfNwxGrfobC7TarU0bhHO+VMxZdpHviEPo7EQewcXAFKvXiIzLZnGzcPNMXb2TjRo2KrM+6xqxsJ8rsYfxb+R+vWu3zicpPMxJda5ciEG/8ZdVGX1m3Ql6UJxvHdgG84f20p2ehKKohB/ejfpV89Rv0nXquiGqESVfszr4sWLREZGsnDhwsredZk42RcNgzJz1BklM8eEs0PJQyQHOw1WWk0JdRS83UpOeA620LeDDbuOFFRCq6uX3tsTQ1KyqsyQlIzOxQmtrR6dmwtaa2sMV1Kui0nBoWlIdTa1VLlZ11BMRuydPVTl9k4epCadva19X42PZemMYRQWGLDR2zP4qbl4+ja6rX1WluzMNEwmI44unqpyR2cPrlwuW7/XLf8YZzcvGrcoSlaZaUXvBafr9+niYb6vpuVlF73edk7q19vO0ZO0K3El1snJTMbe8br3h6MnuZnFfeo2+A12/PgG/323B1qtNWg09Hh4On4hHSq/E7dLTlJWqfTklZqaypIlS26avAwGAwaDei5dr9dX6PHaNbHmkbuK637xc26F9lMeeh08NdCOpGsmNuyp4dUaotK5ewczMmI1hrxMTh7YyLqvpzLs+f/WmgR2O7au+ZKY6HWMf30JOpuKfebqkiN/fEPS+b/oN+oznNz8STi7l52rpmHv7EX960ZtNU5OUlYpd/Jas2bNTe8/e/bW3/6ioqJ4+2314oLIyEjweLG8zeFIXCHnk4qn+6ytir6dONlryPjHSMrJXkv8VeMN9QGycxWMJsU8aiuuo94HFCWu8YPsyCtQWLA2D5Op3E2ucYakZPTe6m/Zem9PCtIzMeUZyE++hqmwEL2Xx3UxHhgSa8c3cTtHNzRaK3Iy1KPDnMwUHJw9S6lVNlbWNrh5BQLg06AFCecPs3/b1/QbPu229lsZHJxc0WqtyEpXvw5ZGSk3jJyut33tQrb9/BVPRSzAr0FTc7mTa1G9zPRknN2KF7JkpafgFxhaia2vOFuHotc797rFOLlZydg5ldxveydPcq5bzJHzj/jCgjz2bJhF3xFzCGzWEwAP36akXD7BX78trH3Jq44eu6qocievwYMHo9FoUJTSD/RobjG8jYiIYMqUKaoyvV7P1C/KPwVnKACDagm8Qnq2icYBVsQnF2UWvQ4CvbX8cbjk/RtNcOmKicb1rTh8tijBaYAmAVb8fqi4jl4HTw+yo9AIX/2SR2HJubDWS9sdQ73+d6rKPHt34druGACUggLSDxzFs1d48ZJ7jQaPu8I5/9l/q7m1JbOytsGnQXPOx0bTuE0fABSTifOx0bTt8XjlPphiwlhYO0bY1tY2+AeHcfroblq0L+q3yWTi9JHddOk7vNR6235ewNaf5jN26pcEhLRQ3ederz5Orp6cProb/6BmAOTlZHHhzCHC+9SOJeNW1jbU829O/OloglsUv97xp3fTvMtjJdbxatCG+NPRtOo+0lwWf2oX3g3aAGAyFmIyFtywBF2j1YJSC7+V1vTKsFqm3Knc19eXlStXYjKZStwOHDhwy33o9XqcnZ1VW0WnDUuyI6aAvu1taB5sha+Hlsf72pKeXXRe1t8mDLalWyud+fb2mALCm+voEGqNt5uGIXfpsbHW8Oexojp6HTw92A4bHXy7JQ9bGw1O9kVbTU9FWznY49w6FOfWRd+S7YPr49w6FNsAXwCavjOF1os+MMef/2I59sEBhEa9hEPTEALHD8d3SH/iZi82x8TNWkTAmEfwf2IwjqEhtJj7FtYOdlxcUvKKtprQvtdoDv3xPUd2ryIl4Qyblr9FgSGXFuEPArB28cvsWP2xOd5YmE/SxeMkXTyO0ZhPVloSSRePc+3KeXPMjtUfc/HUXtJTLnE1PpYdqz/mwqk9hHUYWO39K82d/Ufx57Yf2LdjNUnxZ1i56G3yDbl06PEAAN9+/grrls80x2/7+Ss2/vB/DHnqHdzq+ZGRdpWMtKsY8rKBoi+b3e8ZwZbV8zm6fysJF06yfN4rOLt60bxd7xrpY0ladh/FiT0riN23imtJZ/h91VsU5OfStH3R6711+VT+XF/8erfs9gSXYnfy128LuXblLPs2zeHqpaO06FqU7GxsHfEN6cDutR9x+cyfZKReInbfSk7u/4mgFnfXSB9vSlYbqpR75NWuXTv279/PoEGDSrz/VqOy6rDlQAE2Og1D79Jjp9dwNsHI/DW5qpGSp4sWR9vib1cHTxXiYKehfycbnB00xF81MX9NLlm5RX0J8LIyrzx8Y6SD6vGmLc4mNbPm+uzSrgXhW74x3w6b8SoAF79eyaExEeh962H3v0QGkHvuEnvvH0fYxxEEPTOCvEuJHB73Osmbd5pjElasx6aeO00iny06Sfmv4+y5byz5V0o+p6YmhLa/l5ysVP745f/+d5JyMx6e9JV52jDzWoLqYqZZ6Vf4Omqw+fbeXxey99eFBDTuyLDni56/nMwU1i2ZSnbGFfS2Tnj6N2XIpAUENas9q8/ahPcnOzOVjT/MITM9Gb/AUMZOnW+eNkxLSVCNJqJ/XY6xsIBvZk9W7efuByfQ96FJAPS8bwz5hlx+WBBJXk4mQU3aMnbqF7XquFijNveSl53Kvk1zyMm8iqdfM+4d8yX2/5sGzEq7rJr18QlqS6/hM9i7YRZ7NnyCi2cQ/UZ8irtPE3NMn8dm8uf6mWz59iUMOek4ufnR8Z7JhHWuHSPOmjR37lw++ugjEhMTad26NXPmzKFjx46lxqelpfHaa6+xcuVKUlNTCQwMZNasWdx7771V0j6NUs5M8/vvv5Odnc0999xT4v3Z2dns27ePHj3Kf0WCyXOyyl3H0s16xpG1uqa3DqxjBhTE8lXtOYWo2oztDWv2Weh88224v70VM3+ysHNKKsGUQZU3LZO3Zm6F69reP7Fc8d999x0jRoxg3rx5dOrUiVmzZrFixQpiY2Px8vK6IT4/P5+uXbvi5eXFq6++ir+/P+fPn8fV1ZXWrVtXuN03U+6RV/fu3W96v4ODQ4USlxBCiJuoxtWGM2fO5Mknn2T06NEAzJs3j7Vr17Jw4UJeeeWVG+IXLlxIamoqu3btQqcrOhwTFBRUpW2sm5OhQghR12g0Fd4MBgMZGRmq7frTlf6Wn5/P/v376dOnj7lMq9XSp08foqOjS6yzZs0awsPDmThxIt7e3rRo0YL33nsPo7HqZhkkeQkhhCW4jQUbUVFRuLi4qLaoqKgSHyY5ORmj0Yi3t7eq3Nvbm8TExBLrnD17lh9++AGj0ci6det44403+Pjjj3nnnXcq/Wn4W529qrwQQtQptzFtWNrpSZXFZDLh5eXFF198gZWVFe3atSM+Pp6PPvqo6BzeKiDJSwgh6ji9Xl/mZOXp6YmVlRVJSUmq8qSkJHx8fEqs4+vri06nw8qq+FqwzZo1IzExkfz8fGxsSr4Q9u2QaUMhhLAEt3HMqzxsbGxo164dW7YULwc2mUxs2bKF8PDwEut07dqV06dPY/rHJYdOnjyJr69vlSQukOQlhBCWoRpPUp4yZQpffvklS5Ys4fjx4zz99NNkZ2ebVx+OGDGCiIgIc/zTTz9Namoqzz33HCdPnmTt2rW89957TJxYviX65SHThkIIYQmq8VI+Q4cO5erVq7z55pskJibSpk0bNmzYYF7EceHCBbT/OAYXEBDAxo0bef7552nVqhX+/v4899xzTJ06tcraKMlLCCEsQTVfVX7SpElMmjSpxPu2b99+Q1l4eDi7d++u4lYVk+QlhBAWQKnpi6jWMnLMSwghhMWRkZcQQliCOnp1+IqS5CWEEJZAkpeKJC8hhLAAcsxLTZKXEEJYAhl5qUjyEkIISyAjLxVJ5UIIISyOjLyEEMISVPNJyrWdJC8hhLAAsmBDTZKXEEJYAlmwoSLJSwghLIAiyUtFkpcQQlgCmTZUkVQuhBDC4sjISwghLIBMG6pJ8hJCCEsg04YqGkVRlJpuhBBCiJvL3LehwnWd2t9TiS2pHWrVyOvd5caabkK1e22YFV9tqelWVL+xvWGtrmlNN6PaDSiIpf+oQzXdjGq3fnErJs/JqulmVLtZzzhW2r7kPC+1WpW8hBBClEKOeanIsyGEEMLiyMhLCCEsgIJMG/6TJC8hhLAAslReTZKXEEJYAkleKpK8hBDCAshqQzVJXkIIYQFk2lBNng0hhBAWR0ZeQghhCWTaUEWSlxBCWACZNlST5CWEEBZAzvNSk+QlhBAWQEZeapK8hBDCEsgxLxVJ5UIIISyOjLyEEMICKDLWUJHkJYQQFkCusKEmyUsIISyALNhQk+QlhBAWQJbKq0nyEkIICyAjLzV5NoQQQlgcGXkJIYQFkAUbajLyEkIIC6CgqfBWEXPnziUoKAhbW1s6derEnj17ylRv+fLlaDQaBg8eXKHHLStJXkIIYQEUjbbCW3l99913TJkyhcjISA4cOEDr1q3p168fV65cuWm9c+fO8eKLL9K9e/eKdrPM6ty0YbtGGjo30+BoC0lpsGm/icuppceHBkCPllpcHSA1E7b+ZeJMQvH993XS0DpY/eKfSVBY/pupajpQQQd+W8rezQvIzriKV/1Qej/yBr5BrUqMTb58ip2//B9JF46SkRrPXQ9H0L7XKFXMwR3LiNnxLRmp8QB4+Damy70TCGneo6q7Umbu3doT8sIYXNq2wNbPi30PTSBpzZab17mzI2EzXsExrDF5FxM4HfU5l75epYoJfHo4IVPGoPepR8ahExydPJ30vYersisV8sQD3tzTwx0HeyuOncrm06/juZyUX2p8iyYOPHxvPRoF2uHhpmPa/50j+kCGKsZWr2X0EB+6tHXGydGapKv5/PRrMuu23eRDVAP6d7Khc3Nr7PQa4hKMrNhmIDlduWmdbi119Gqrw8lew+VkEz/uMHAhqehzbK+HezrZENrAGlcnDdm5CofPFrJudz55pT+l1ao6VxvOnDmTJ598ktGjRwMwb9481q5dy8KFC3nllVdKrGM0Gnnsscd4++23+f3330lLS6vSNtapkVezAA197tDw+xGFBRtNXElTGNZTi72+5Hh/D3ggXMtfZxW+2mjiZLzCkG5a6rmo485cVpi12mjeVu+qXYnrxL51bP8xii4DJjIiYhX1/ENZMWcM2ZkpJcYX5Ofi6lmfOwe/gINzvRJjnFx96DH4RUa8spInpv5IYJPOrJo3keTLp6qyK+Vi5WBPxqFYjjz7dpni7YLq02HNfFK2/8nO9oOIm7OElvPfwfPubuYY3yH9afZRBKfemcvOjg+QeegEndYuwKaee1V1o0KG3FuP++/2ZM6SeCZPO02ewcQ7LwSj05X+D85Wr+XshVw++ya+1JinHvWlfUsnPvziIk+9GsvqTclMeNyfTm2cq6IbFdK7rY47W+tYsc3AJ9/nkl8A4wfZYW1Vep07GlszuLsNG/bkM2N5DvHJJsbfb4ejXdHz5eygxcVBy087DXywNIdlvxoIbWDNsN621dSrW6uukVd+fj779++nT58+5jKtVkufPn2Ijo4utd60adPw8vJizJgxFe5jedSp5NUpVEPMGYVDcQrJGbBur0JhIbQOKfkD3bGphjMJsPuEQkoG/HZYIfEatG+sji80QXZe8ZZXUB29Kbt9WxfRqusjtAx/CE/fRvR99G10NrYc2fVjifG+Qa3o+eBUmrUfgJW1TYkxjVr1IqRFD9y8gnD3Dqb7oOex0dtzOS6mCntSPlc37uBk5CySfvq1TPGBTw0jN+4Sx1/+gKwTZzn/2VISf9xI8HOjzDHBk0dzccH3XFqykqzjZzg8IRJjTh4Box6qol5UzOC+nixfk8Tugxmcu5THjC8v4uGmo0vb0pPMvsOZfL0yiV3Xjbb+qVkjB3794xqHT2RzJbmA9b+lcvZiLk1D7KqiGxVyZxsdm/bmcyTOSEKKiaWb83Bx0NAypPSJpJ5tdEQfLWDP8UKSrims2GYgv1ChU1hRncRUE4vW53H0nJGUDIVTl4ys3W2gRbAV2jqwTsJgMJCRkaHaDAZDibHJyckYjUa8vb1V5d7e3iQmJpZYZ+fOnSxYsIAvv/yy0ttemjqTvLRa8HWDuCT11EFckkJ9j5Lfff4emhvizyYq+F8XH+gFkwdrGX+vlnvaabAr+f99jTAW5pN44SiBTbuYyzRaLYGhXbgcd7BSHsNkMnJ831oK8nPwC7mjUvZZE1w7tyF5q/qb49XNO3Hr3AYAjU6HS9vmJG/ZVRygKCRv3YVr59rTb596Nri76jh4LMtclpNrIvZMDqENHW5r38dPZ9O5jTMerkX/1FuFOuDvrefAkaxb1KweHs4aXBy0nLxoNJfl5cP5JBNBPiX/O7PSQn0vdR0FOHnRSJBP6cM1OxsNeflguvlsZLW5nQUbUVFRuLi4qLaoqKhKaVdmZiZPPPEEX375JZ6enpWyz7KoM8e87G1Aq9WQnacuz84Dj1K+jDraUmK8wz++ZJ5NgNiLJtKywc0RerbSMqyHhsW/mlBqwZs6N+saismIvbOHqtzeyYPUpLO3te+r8bEsnTGMwgIDNnp7Bj81F0/fRre1z5qk9/bEkJSsKjMkJaNzcUJrq0fn5oLW2hrDlZTrYlJwaBpSnU29KTeXoo/ttfRCVfm1jELzfRX1+X8v8+wof/47K4zCQgVFUZi96BJHTmbf1n4ri5N90RfLzBz1hy8zx4SzQ8lfUh3sNFhpNSXUUfB2KznhOdhC3w427DpSe6ZZbuck5YiICKZMmaIq0+tLPp7i6emJlZUVSUlJqvKkpCR8fHxuiD9z5gznzp1j4MCB5jKTqejQirW1NbGxsTRs2LDCbS9Nud/pubm57N+/H3d3d8LCwlT35eXl8f333zNixIib7sNgMNwwZC16ImtfLj12ofgNfzUdrqSZmDjQikAvOJd0k4p1gLt3MCMjVmPIy+TkgY2s+3oqw57/r0UnMEt0V7grz4z0N9+O/ORclT3W/X08CG3owFuz4khKLqBlUwcmPOFPSlohMceqf/TVrok1j9xV/E/2i59zq/wx9Tp4aqAdSddMbNhTS1ZrcHsLNvR6fanJ6no2Nja0a9eOLVu2mJe7m0wmtmzZwqRJk26IDw0N5fBh9YKm119/nczMTGbPnk1AQECF230z5coWJ0+epG/fvly4cAGNRkO3bt1Yvnw5vr6+AKSnpzN69OhbJq+oqCjeflt9kD0yMhJd6BvlbH6xnHwwmRQcrju+6mAL2aW837PyKFc8QFo2ZOcpuDlqOJdU80MvO0c3NForcjLUo4WczBQcnG9vCG9lbYObVyAAPg1akHD+MPu3fU2/4dNua781xZCUjN5b/ZzovT0pSM/ElGcgP/kapsJC9F4e18V4YEhUj9iq0+6DGZw4k2O+rbMu+ifm5mKtGn25OVtz5kLeDfXLykanYeTDPkyfc569f2UCcO5SHiEN7Hiof70aSV5H4go5n1Q83WdtVdR3J3sNGf8YSTnZa4m/aryhPkB2roLRpJhHbcV11PuAosQ1fpAdeQUKC9bmYapFa7Oq8yTlKVOmMHLkSNq3b0/Hjh2ZNWsW2dnZ5tWHI0aMwN/fn6ioKGxtbWnRooWqvqurK8AN5ZWpXOPQqVOn0qJFC65cuUJsbCxOTk507dqVCxculOtBIyIiSE9PV20RERHl2sf1TCZIuAZB3uoXOMhbw6WUkpNMfIpC8HXxwT4a4kuJB3CyK1pWm5Vb84kLihKMT4PmnI8tPpajmEycj43GL7iSj9MoJoyFteebaHml7Y7Bo1dnVZln7y5c2x0DgFJQQPqBo3j2Ci8O0GjwuCuctN2Vc/ywInLzTCRcyTdvFy4bSE0roE2YoznG3lZL04b2nDhT8ek9aysNOmstynX/sE0mpcYWLRgKIDldMW+JqSbSs000Dig+VqXXQaC3lnOJJWcaowkuXTHRuH5xHQ3QJMCKc4nFCU+vg6cH2WE0wle/5FFYci6sMYqiqfBWXkOHDmXGjBm8+eabtGnThpiYGDZs2GBexHHhwgUSEhJusZeqVa6R165du/j111/x9PTE09OTn3/+mQkTJtC9e3e2bduGg0PZDhaXPoS9vXfLnycU7u+sISEVLqcqdGyiQWcNh84WJZqBnTRk5sL2Q0W398QqPNFbQ6emGk5fVggL1ODrVrRKEUBnDd2bazhxSSE7r+iYV6/WWlIz4WzJi25qRPteo1n39VR8AlvgG9iKfduWUGDIpUX4gwCsXfwyTq7e3Dn4BaBokUdywpmiv435ZKUlkXTxODZ6e/NIa8fqjwlufifO7r7k52VzfO8vXDi1hyGTFtRMJ0tg5WCPQ6MG5tv2wfVxbh1Kfmo6eRcTaPrOFGz9vflr9FQAzn+xnMAJjxEa9RIXF/+I512d8R3Sn733jzPvI27WIlov/IC0/UdI33uIoGdHYu1gx8UlK6u9fzezelMywwZ6EZ+YT1JyPk886E3KtQLVSsKol4PZtT+Dn7cUjcpt9Vr8vItXG3l72hDSwJbMLCNXUwvIyTNx6EQWY4b6YigwcSU5n5ahjvTu6saX316u9j6WZkdMAX3b23A1zURqhsK9nW1Izy46L+tvEwbbcuiskZ2Hio5ZbY8pYHgfPRevmLiQZKRHGxtsrDX8eayojl4HTw+2w8YavtmUh62NBtv/PVVZuUqtOL5d3SZNmlTiNCHA9u3bb1p38eLFld+g65QreeXm5mJtXVxFo9Hw+eefM2nSJHr06MGyZcsqvYHlcfxi0bRhj5YaHGw1JKXB8u0msv93eM3FQYNC8bswPgVWR5vo2VJLz1YaUjNhxU4TV9OL7lcU8HLV0CpYg60OMvMgLlHht0MKxlo0nRDa/l5yslL545f/+99Jys14eNJX5mnDzGsJaLTFg+ys9Ct8HTXYfHvvrwvZ++tCAhp3ZNjz3wBF047rlkwlO+MKelsnPP2bMmTSAoKada3Wvt2MS7sWhG/5xnw7bMarAFz8eiWHxkSg962HXYCv+f7cc5fYe/84wj6OIOiZEeRdSuTwuNdJ3rzTHJOwYj029dxpEvls0UnKfx1nz31jyb9S8jlzNWXFuqvY6rU8O9ofR3srjp7M5o2P4ygoKH5/+3rpcXYq/rw2Drbjw1eKD5yPG+4HwOadqcz86hIA739+gVEP+/DyuAY4OVhxJSWfJT8msrYWnaS85UABNjoNQ+/SY6fXcDbByPw1uaqRkqeLFkfb4g/pwVOFONhp6N/JBmcHDfFXTcxfk2ueQQnwsjKvPHxjpPpL+LTF2aRm1nz2kl9SVtMoStm/U3Ts2JFnnnmGJ5544ob7Jk2axNKlS8nIyMBorNgI6t3ltWycXg1eG2bFVze/KESdNLY3rNU1relmVLsBBbH0H3WopptR7dYvbsXkObVjuX11mvWM462DyujkmfIdnvmnJg0b3DrIwpQrlT/wwAN8++23Jd736aef8uijj1KOXCiEEKKMqvvCvLVduZJXREQE69atK/X+zz77zLy+XwghROWR5KVW+06sEkIIcYO6moQqSo4ACiGEsDgy8hJCCAtQkfO16jJJXkIIYQFk2lBNkpcQQlgASV5qkryEEMICSPJSkwUbQgghLI6MvIQQwgLIgg01SV5CCGEBTDJtqCLJSwghLIAc81KT5CWEEBZApg3VJHkJIYQFkJGXmqw2FEIIYXFk5CWEEBZApg3VJHkJIYQFkGlDNUleQghhAWTkpSbJSwghLID8zK+aJC8hhLAAMvJSk9WGQgghLI6MvIQQwgLIgg01SV5CCGEBZNpQTZKXEEJYABl5qUnyEkIIC2BSaroFtYskLyGEsAAy8lLTKIoi+VwIIWq5347mVLhuj+b2ldiS2qFWjbx6Phxd002odtt/CGfNPmNNN6Pa3d/eiv6jDtV0M6rd+sWtWKtrWtPNqHYDCmLpNvC3mm5Gtdv5c49K25cs2FCrVclLCCFEyWSOTE2SlxBCWACTHPNSkeQlhBAWQKYN1SR5CSGEBZBpQzW5tqEQQgiLIyMvIYSwAHKel5okLyGEsAByhQ01SV5CCGEBZMGGmiQvIYSwALJgQ00WbAghhAUwoanwVhFz584lKCgIW1tbOnXqxJ49e0qN/fLLL+nevTtubm64ubnRp0+fm8ZXBkleQgghVL777jumTJlCZGQkBw4coHXr1vTr148rV66UGL99+3YeffRRtm3bRnR0NAEBAfTt25f4+Pgqa6MkLyGEsACKUvGtvGbOnMmTTz7J6NGjCQsLY968edjb27Nw4cIS45cuXcqECRNo06YNoaGhfPXVV5hMJrZs2XKbvS6dJC8hhLAAiqKp8FYe+fn57N+/nz59+pjLtFotffr0ITq6bBdPz8nJoaCgAHd393I9dnnIgg0hhLAAt7NU3mAwYDAYVGV6vR69Xn9DbHJyMkajEW9vb1W5t7c3J06cKNPjTZ06FT8/P1UCrGwy8hJCCAtwO9OGUVFRuLi4qLaoqKgqaef777/P8uXLWbVqFba2tlXyGCAjLyGEsAi3c4WNiIgIpkyZoioradQF4OnpiZWVFUlJSarypKQkfHx8bvo4M2bM4P333+fXX3+lVatWFW5vWcjISwgh6ji9Xo+zs7NqKy152djY0K5dO9Vii78XX4SHh5f6GB9++CHTp09nw4YNtG/fvtL7cD0ZeQkhhAWozstDTZkyhZEjR9K+fXs6duzIrFmzyM7OZvTo0QCMGDECf39/89TjBx98wJtvvsmyZcsICgoiMTERAEdHRxwdHaukjZK8hBDCAlTnFTaGDh3K1atXefPNN0lMTKRNmzZs2LDBvIjjwoULaLXFE3eff/45+fn5PPzww6r9REZG8tZbb1VJGyV5CSGEBajuy0NNmjSJSZMmlXjf9u3bVbfPnTtX9Q26jiQvIYSwACa5MK+KJC8hhLAAcmFeNVltKIQQwuLU2ZHX6KEB3NfHC0d7a47EZjDzizjiE/NKjR/+gB93dvKggb8dhnwTR2Mzmf/f81y8XFznvj5e9OnuSeNgBxzsrblvxB6ycozV0Z1b+mPTMn5bu5DM9GR8GzRl8MjXaNCw5PMs/ty6gv07fyLx4mkA/IPD6D90sipeURQ2/fgpf25bQW52JkFN7uDB/7xJPZ+g6uhOuTzxgDf39HDHwd6KY6ey+fTreC4n5Zca36KJAw/fW49GgXZ4uOmY9n/niD6QoYqx1WsZPcSHLm2dcXK0JulqPj/9msy6balV3Z1bcu/WnpAXxuDStgW2fl7se2gCSWtufg059zs7EjbjFRzDGpN3MYHTUZ9z6etVqpjAp4cTMmUMep96ZBw6wdHJ00nfe7gqu1JhYx4LYmBfH5wcrDl8PIMZn53iUkJuqfGPPxxAjy6eBPrbY8g3cfhEBp8vPsvF+OI6L01sTPvWbni625CTZ+TI8Qw+X3KWC5dK3291kpGXWp0ceT062I+H7vVh5hdnefrVw+QaTHz0RjNsdKXPGbcJc2H1hkQmRBzmxWnHsLLS8NEbYdjqi58iW72WPQfTWLqy6q6UXBEx0ev5eekH3P3gBCa/8wN+DUL56v2nyEpPKTH+zPE9tAkfwLjXFjHp7WW4evjw5ftPkp5afFLi9l8WsHPjf3lwdCTPTFuOjd6Or95/ioJ8Q4n7rClD7q3H/Xd7MmdJPJOnnSbPYOKdF4LR3eS1ttVrOXshl8++Kf11fOpRX9q3dOLDLy7y1KuxrN6UzITH/enUxrkqulEuVg72ZByK5cizb5cp3i6oPh3WzCdl+5/sbD+IuDlLaDn/HTzv7maO8R3Sn2YfRXDqnbns7PgAmYdO0GntAmzqVd216SrqsYcCePg+f2Z8doqnXjxIbp6RmdNa3vTzfUcLV1auvcy4lw7y/BuHsLbS8Mm0VqrPd+zpLN6bHctjE/byQuRhNBr4ZFortLXkv6RJqfhWF9WSl6VyPTzAl29+vMQfe69x9nwOUXNO4+lmQ7eOpX8QX373OBu2X+XcpVzOnM/h/bmn8amnp0mIgznmh7WJLFt9mWOnsqqjG2W2Y/1iOt01hA49HsS7fiMe/E8kOr0te35bWWL88Ikf0eXuR/EPaoaXXwhDnpyOYjJx6uhuoGjU9fuGr+k9eBwt2vfGr0FThj39PhlpVzi6v+quEl0Rg/t6snxNErsPZnDuUh4zvryIh5uOLm1LTzL7Dmfy9cokdl032vqnZo0c+PWPaxw+kc2V5ALW/5bK2Yu5NA2xq4pulMvVjTs4GTmLpJ9+LVN84FPDyI27xPGXPyDrxFnOf7aUxB83EvzcKHNM8OTRXFzwPZeWrCTr+BkOT4jEmJNHwKiHqqgXFTfkfn++/v48O/9M4cy5bN755AQe7nq6d/Ystc4Lbx1m/ZYk4i7kcPpcNu/NisXHy5amjZzMMWs2JvDX0XQSrxg4eSaLL/97Du96tvh4Vd0ljsqjui7MaynqXPLy9dLj4WbD/kPp5rLsHCPHTmUR1sTpJjXVHO2LZlQzsworvY2VqbAwn/i4YzRu0dlcptVqadwinPOnYsq0j3xDHkZjIfYOLgCkXr1EZloyjZsXn01vZ+9Eg4atyrzP6uBTzwZ3Vx0HjxV/mcjJNRF7JofQhg43qXlrx09n07mNMx6uRe+DVqEO+HvrOXCkdn1xKQvXzm1I3qq+GvjVzTtx69wGAI1Oh0vb5iRv2VUcoCgkb92Fa+c7qrGlt+bnbYunu569MdfMZdk5Ro6dzKBFaNlHxQ4OVgBkZBaUeL+tXsu9fXy4nJjLleTaMdtQnT+JYgnKfczr+PHj7N69m/DwcEJDQzlx4gSzZ8/GYDDw+OOP06tXr6poZ5m5u+kASE1Tvymvpefj7qor0z40Gpg0OojDxzOIu1g75rtLk52ZhslkxNFF/a3T0dmDK5fPlmkf65Z/jLObF41bFCWrzLRkAJyu36eLh/m+2sDNpejtey1d/QXjWkah+b6K+vy/l3l2lD//nRVGYaGCoijMXnSJIyezb2u/NUHv7YkhSf26GZKS0bk4obXVo3NzQWttjeFKynUxKTg0DanOpt6Su5sNANeu/3yn5ZvvuxWNBp59shGHjqUTdyFHdd8D9/rx9KgQ7O2sOH8ph8lvHKKwsHb896+r038VVa5P+IYNGxg0aBCOjo7k5OSwatUqRowYQevWrTGZTPTt25dNmzbdMoGVdnn+iujT3ZMXnir+gL0SVbZL9t/M5LHBBAfY8czrR297X7Xd1jVfEhO9jvGvL0FnU7HXoLrcFe7KMyP9zbcjPzlXZY91fx8PQhs68NasOJKSC2jZ1IEJT/iTklZIzDHLG31Zqrt7ePHSxCbm2y9Pu/0FJFPGNyakgQMTph684b5N25PYe/AaHu42PPpAfaZPDePplw+SXyCZo7YpV/KaNm0aL730Eu+88w7Lly9n+PDhPP3007z77rtA0ZWL33///Vsmr6ioKN5+W32wOTIyEuhXvtYDf+xN5fg/jkHprIvmd91ddarRl5uLDafP3fpb83Njgglv58azbx7lamrpK9ZqCwcnV7RaK7LS1d+sszJSbhg5XW/72oVs+/krnopYgF+DpuZyJ9eiepnpyTi71SveZ3oKfoGhldj68tl9MIMTZ4q/Kf/9Wru5WKtGX27O1py5UPrK0lux0WkY+bAP0+ecZ+9fmQCcu5RHSAM7Hupfz+KSlyEpGb23+r2g9/akID0TU56B/ORrmAoL0Xt5XBfjgSGxZkfaO/ekcOzkPvNtG13RkQ43Vx0p14o/n26uNpw+e+vX5flxjejSwZ1JEX9xNeXGz3d2jpHsnFwuJeRyNDaD9d925c5wT37dcbUSenN76ur0X0WV65jX0aNHGTVqFACPPPIImZmZqmtZPfbYYxw6dOiW+4mIiCA9PV21RURElK/l/5ObZyI+Mc+8nbuUS8q1fNq2dDHH2NtZEdbYkWMnM2+6r+fGBNOtozvPv3WMxCu1Y577VqytbfAPDuP0/xZbQNEVoE8f2U1g4zal1tv28wK2rJrH2Je/ICCkheo+93r1cXL1VO0zLyeLC2cO3XSfVS03z0TClXzzduGygdS0AtqEFV/4095WS9OG9pw4U/HpPWsrDTprLYpJXW4yKWgt8Nh32u4YPHp1VpV59u7Ctd0xACgFBaQfOIpnr39cMVyjweOucNJ23zg6qU65uUbiE/LMW9yFHJJTDbRv7WaOsbezIqyJM0dOlL4AB4oS153hnjz32iESkm795UZD0RSjTlc7lgbIMS+1ch8Y0GiKPr1arRZbW1tcXIqThJOTE+np6aVVNSvtFzwryw9rE3jiofpcSsgj4YqBMcMCSL6Wz849xefofBwZxs4/U1m1oejqx5PHBtOnuyevfRBLbp7RfHwsK8dIfn7RfzF3Vx3urjr8fYpWHwUH2pObayQpOb9GF3bc2X8U382PoH5wCwIatuT3DV+Tb8ilQ48HAPj281dwcfPi3mFFv+ez7eev2PjDHIZP/Ai3en5kpBV9q9Tb2qO3dUCj0dD9nhFsWT0fT59A3OvVZ+MP/4ezqxfN2/WusX6WZPWmZIYN9CI+MZ+k5HyeeNCblGsFqpWEUS8Hs2t/Bj9vKTqmY6vX4uddfHzE29OGkAa2ZGYZuZpaQE6eiUMnshgz1BdDgYkryfm0DHWkd1c3vvz2crX38XpWDvY4NGpgvm0fXB/n1qHkp6aTdzGBpu9Mwdbfm79GTwXg/BfLCZzwGKFRL3Fx8Y943tUZ3yH92Xv/OPM+4mYtovXCD0jbf4T0vYcIenYk1g52XFxS8orVmrRiTTwjhzbg4uVcEpLyGPt4ECmpBn7fXTxKnPVOK3ZEJ7NybdHr9cLTjehzpzcR7x4hJ7fwhs+3n7ctvbrXY+/Ba6RlFFDPQ8/jDwdgMJiI3lfz5/aBHPO6XrmSV1BQEKdOnaJhw4YAREdH06BB8YfowoUL+Pr6Vm4LK+Db1Zex1Vvx4rgQHB2sOXwig5ffOa6at/b31uPiXNz9wfcU/cja7GnNVft6/9PTbNhe9M/9/r7ejHokwHzfnOktboipCW3C+5OdmcrGH+aQmZ6MX2AoY6fON08bpqUkoNEUf3uM/nU5xsICvpk9WbWfux+cQN+Hii7E2fO+MeQbcvlhQSR5OZkENWnL2Klf1LrjYivWXcVWr+XZ0f442ltx9GQ2b3wcR8E/XmtfLz3OTsWvdeNgOz58paH59rjhfgBs3pnKzK8uAfD+5xcY9bAPL49rgJODFVdS8lnyYyJra8FJyi7tWhC+5Rvz7bAZrwJw8euVHBoTgd63HnYBxZ/D3HOX2Hv/OMI+jiDomRHkXUrk8LjXSd680xyTsGI9NvXcaRL5bNFJyn8dZ899Y8m/UvK5gjVp6Y8XsbW14uVJTYo+38fSeSHysPrz7WOHq3PxAq0H7i06VvppVBvVvt6ddYL1W5IwFJho3dyFR+6vj5OjNalp+fx1NJ3xLx8kLb3kFYnVra6OoCpKoyhlf0rmzZtHQEAAAwYMKPH+V199lStXrvDVV19VqDE9H46+dVAds/2HcNbsqx1X6ahO97e3ov+oW08x1zXrF7dira7prQPrmAEFsXQb+FtNN6Pa7fy5R6Xta/6mitcd17fSmlFrlGvkNX78+Jve/957791WY4QQQoiyqLPXNhRCiLpEpg3VJHkJIYQFkOSlJslLCCEsgKw2VJPkJYQQFqAca+tKYIEnKN6CJC8hhLAAMm2oVjtOHRdCCCHKQUZeQghhAUymW8f8m0jyEkIICyDThmqSvIQQwgLIakM1SV5CCGEBZOSlJslLCCEsgHJbQ6+6t1ReVhsKIYSwODLyEkIICyDHvNQkeQkhhAWQY15qkryEEMICmGTopSLJSwghLICMvNQkeQkhhAWQ5KUmqw2FEEJYHBl5CSGEBTDJ0EtFkpcQQlgARS7MqyLJSwghLMDt/Rhl3SPJSwghLID8JIqaLNgQQghhcWTkJYQQFkCmDdVk5CWEEBbApFR8q4i5c+cSFBSEra0tnTp1Ys+ePTeNX7FiBaGhodja2tKyZUvWrVtXsQcuI40i6VwIIWq91xYaKlz33f/oyxX/3XffMWLECObNm0enTp2YNWsWK1asIDY2Fi8vrxvid+3axZ133klUVBT33Xcfy5Yt44MPPuDAgQO0aNGiwu2+mVqVvH47mlPTTah2PZrbM/OnWvMSVJspgzRMnpNV082odrOecaTbwN9quhnVbufPPVira1rTzah2AwpiK21fry6oePJ6b0z5klenTp3o0KEDn376KQAmk4mAgACeeeYZXnnllRvihw4dSnZ2Nr/88ou5rHPnzrRp04Z58+ZVuN03I9OGQghhAUwmpcJbeeTn57N//3769OljLtNqtfTp04fo6OgS60RHR6viAfr161dqfGWQBRtCCFHHGQwGDAb1yE2v16PX3zgiS05Oxmg04u3trSr39vbmxIkTJe4/MTGxxPjExMTbbHnpZOQlhBAWQFGUCm9RUVG4uLiotqioqJru0m2RkZcQQliA27k8VEREBFOmTFGVlTTqAvD09MTKyoqkpCRVeVJSEj4+PiXW8fHxKVd8ZZCRlxBCWACTolR40+v1ODs7q7bSkpeNjQ3t2rVjy5YtxY9tMrFlyxbCw8NLrBMeHq6KB9i8eXOp8ZVBRl5CCGEBqnNh+JQpUxg5ciTt27enY8eOzJo1i+zsbEaPHg3AiBEj8Pf3N089Pvfcc/To0YOPP/6YAQMGsHz5cvbt28cXX3xRZW2U5CWEEBagvKsGb8fQoUO5evUqb775JomJibRp04YNGzaYF2VcuHABrbZ44q5Lly4sW7aM119/nVdffZXGjRuzevXqKjvHCyR5CSGEKMGkSZOYNGlSifdt3779hrIhQ4YwZMiQKm5VMUleQghhAWrP5SRqB0leQghhAZRqnDa0BJK8hBDCAphk6KUiyUsIISyAjLzUJHkJIYQFkOSlJicpCyGEsDgy8hJCCAsgAy81SV5CCGEBZNpQTZKXEEJYgFr0u8G1giQvIYSwANV5eShLIMlLCCEsgIy81GS1oRBCCIsjIy8hhLAAsmBDTZKXEEJYAEleapK8hBDCAsi1DdXqXPLatv47Nq1eQnpaCvWDmvDo2KkENy75B9F+37yS6O2/cPnCaQAaNGzGA489c0N8wqWz/Pj1bE4eO4DJWIhv/RDGvzwDj3q+Vd6fsjqyayl//baA3MxkPHxD6TrodbwatCo1/syhDezbOJvMa/G4eAbSqf+LNGjWw3x/gSGbP9d/zLmjW8jLTsPJvT4tuz5BWPiw6uhOufXvZEPn5tbY6TXEJRhZsc1AcvrNP+zdWuro1VaHk72Gy8kmftxh4EKSCQB7PdzTyYbQBta4OmnIzlU4fLaQdbvzycuvjh6VzZjHghjY1wcnB2sOH89gxmenuJSQW2r84w8H0KOLJ4H+9hjyTRw+kcHni89yMb64zksTG9O+tRue7jbk5Bk5cjyDz5ec5cKl0vdbHdy7tSfkhTG4tG2BrZ8X+x6aQNKaLTevc2dHwma8gmNYY/IuJnA66nMufb1KFRP49HBCpoxB71OPjEMnODp5Oul7D1dlVypERl5qdWrBxt6dG1mx6GPue2Qcr89YRkBQE2ZPm0BGWmqJ8bFH9tGx2z28MO1LpkYtwd3Dh1lvP821lCvmmCuJF/nw1f/gUz+YF6d9yZuffM+AIU+i0+mrq1u3dDpmHdE/v0+7PhN56LmVuPs2Ze2CseRmpZQYn3juAFuWvUDTDg/z0HOrCGreh41fTyI18aQ5ZtfP73Mxdie9hn3I0BfX0rLbCHb+NJ1zR7dWV7fKrHdbHXe21rFim4FPvs8lvwDGD7LD2qr0Onc0tmZwdxs27MlnxvIc4pNNjL/fDkc7DQDODlpcHLT8tNPAB0tzWPargdAG1gzrbVtNvbq1xx4K4OH7/Jnx2SmeevEguXlGZk5riY1OU2qdO1q4snLtZca9dJDn3ziEtZWGT6a1wlZf/K8g9nQW782O5bEJe3kh8jAaDXwyrRXaGv5vYeVgT8ahWI48+3aZ4u2C6tNhzXxStv/JzvaDiJuzhJbz38Hz7m7mGN8h/Wn2UQSn3pnLzo4PkHnoBJ3WLsCmnntVdaPCFEWp8FYXVcrbsbY8OZt//i/d7n6Qrr0H4RfQkMfGvYaN3pY/tq4uMX7s8+/Rs/8jBAQ3xbd+MCMmvImiKJw49Kc5ZvXST2nRrhsPj5hMg5BQvHwCaNOxJ86utefNffj3xTTrNITQDg/h5t2IOx98G2udLSf2/lhy/M5vCGjSjTY9x+Dm3ZAO/Z7D0z+MI38sNccknY+hSbvB+DXshJN7fcI6D8XDtylXLh6qrm6V2Z1tdGzam8+ROCMJKSaWbs7DxUFDy5DSJxZ6ttERfbSAPccLSbqmsGKbgfxChU5hRXUSU00sWp/H0XNGUjIUTl0ysna3gRbBVmhLzw3Vasj9/nz9/Xl2/pnCmXPZvPPJCTzc9XTv7FlqnRfeOsz6LUnEXcjh9Lls3psVi4+XLU0bOZlj1mxM4K+j6SReMXDyTBZf/vcc3vVs8fGq2cR9deMOTkbOIumnX8sUH/jUMHLjLnH85Q/IOnGW858tJfHHjQQ/N8ocEzx5NBcXfM+lJSvJOn6GwxMiMebkETDqoSrqhagslZK89Ho9x48fr4xdVVhhQQEXzhynWatO5jKtVkuzVp04G1u2f7j5+XkYjYU4OLkAYDKZOLx/J96+DZg1bQIvjOrFe1Of4OCf26qkDxVhLMznavxR/Bt1MZdptFrqNw4n6XxMiXWuXIjBv3EXVVn9Jl1JulAc7x3YhvPHtpKdnoSiKMSf3k361XPUb9K1KrpRYR7OGlwctJy8aDSX5eXD+SQTQT4lv72ttFDfS11HAU5eNBLkU/pwzc5GQ15+7bjGnJ+3LZ7uevbGXDOXZecYOXYygxahzmXej4NDUX8zMgtKvN9Wr+XePj5cTszlSrLh9hpdzVw7tyF5a7Sq7Ormnbh1bgOARqfDpW1zkrfsKg5QFJK37sK18x3V2NKyMZmUCm91UbmOeU2ZMqXEcqPRyPvvv4+HhwcAM2fOvOl+DAYDBoP6g6DX3940XFbmNUwm4w0jIidXDxLiz5VpHz9+PRsXt3rmBJiZnoohL4cNqxYxaPhEHnriOY4c/IN5H77AlGlf0LR5+9tqc2XIy76GYjJi5+ShKrdz9CTtSlyJdXIyk7F3VMfbO3qSm5lsvt1t8Bvs+PEN/vtuD7Raa9Bo6PHwdPxCOlR+J26Dk33RMCgzR/0Bzcwx4exQ8hDJwU6DlVZTQh0Fb7eSE56DLfTtYMOuIyX/k69u7m42AFxLU7fnWlq++b5b0Wjg2ScbcehYOnEXclT3PXCvH0+PCsHezorzl3KY/MYhCgst65+g3tsTQ1KyqsyQlIzOxQmtrR6dmwtaa2sMV1Kui0nBoWlIdTa1TOSYl1q5ktesWbNo3bo1rq6uqnJFUTh+/DgODg5oNLeeU4mKiuLtt9Xz1pGRkdw15OXyNKdSrV+5kL1/bOTFaV+isylKpIpSdPC+Tcee3D3wcQACgpty5sRf7Nj4Q61IXlXlyB/fkHT+L/qN+gwnN38Szu5l56pp2Dt7Uf+6UVt1atfEmkfuKv6i88XPVb+IQK+DpwbakXTNxIY9NbNa4+4eXrw0sYn59svTbn9BwZTxjQlp4MCEqQdvuG/T9iT2HryGh7sNjz5Qn+lTw3j65YPkF8g/0JpSWw7P1BblSl7vvfceX3zxBR9//DG9evUyl+t0OhYvXkxYWFiZ9hMREXHDKE6v17P7tLGUGrfm6OSGVmt1w+KMzLQUXFw9SqlVZNPqr9mwchHPvzWP+kHF/yAcndzQWlnjW1/9Lcy3fginj9/4ga8Jtg5uaLRW5Gaqvz3mZiVj51TysQ97J09yrlvMkfOP+MKCPPZsmEXfEXMIbNYTAA/fpqRcPsFfvy2s0eR1JK6Q80nF7xNrq6IvS072GjL+MZJystcSf7Xk91N2roLRpJhHbcV11PuAosQ1fpAdeQUKC9bmYTJVVk/KZ+eeFI6d3Ge+baMrGiG6uepIuVacUN1cbTh9NuuW+3t+XCO6dHBnUsRfXE25MSFn5xjJzsnlUkIuR2MzWP9tV+4M9+TXHVcroTfVw5CUjN5b/RnQe3tSkJ6JKc9AfvI1TIWF6L08rovxwJCoHrHVBkpNvflqqXId83rllVf47rvvePrpp3nxxRcpKKjYFIper8fZ2Vm13e60obVOR4OGzVSLLUwmE8cP7SGkaelLxjesWswvP3zJc2/MJahR8xv2GdQojMTL51XlSZfP4+FVO5bJW1nbUM+/OfGni+f2FZOJ+NO78Q5sU2IdrwZtVPEA8ad24d2gKN5kLMRkLECjUb89NFotKDX7ATIUQHK6Yt4SU02kZ5toHFB8rEqvg0BvLecSS26r0QSXrphoXL+4jgZoEmDFucTihKfXwdOD7DAa4atf8iis+Her25abayQ+Ic+8xV3IITnVQPvWbuYYezsrwpo4c+RExk339fy4RtwZ7slzrx0iISnvlo+toWiKUaezrMXJabtj8OjVWVXm2bsL13bHAKAUFJB+4CievcKLAzQaPO4KJ2137fhy+k9yzEut3O/GDh06sH//fq5evUr79u05cuRImaYKq8PdAx/n919XsWvbGhIunWXp/PfIN+TStdcgABbOfp2V//0/c/yGlYtY8+1njJwYiYeXH+nXkkm/lkxebvH8f79BI9n3x0Z+37ySKwkX2LpuOYf27aDHPY9Ue/9K07L7KE7sWUHsvlVcSzrD76veoiA/l6btHwRg6/Kp/Ln+4+L4bk9wKXYnf/22kGtXzrJv0xyuXjpKi66PAWBj64hvSAd2r/2Iy2f+JCP1ErH7VnJy/08Etbi7Rvp4MztiCujb3obmwVb4emh5vK8t6dlF52X9bcJgW7q10plvb48pILy5jg6h1ni7aRhylx4baw1/Hiuqo9fB04PtsNHBt1vysLXR4GRftNWStzsr1sQzcmgDunb0ICTQgdenhJKSauD33cWjhlnvtOLBAX7m2y883Yi+Pb15e8ZxcnILcXfV4e6qw8am6F+Bn7ctjz8cQNOGjnjX09Mi1Jnpr4RhMJiI3lfyKSfVxcrBHufWoTi3DgXAPrg+zq1DsQ0o+iLZ9J0ptF70gTn+/BfLsQ8OIDTqJRyahhA4fji+Q/oTN3uxOSZu1iICxjyC/xODcQwNocXct7B2sOPikpXV2jdRfhU6SdnR0ZElS5awfPly+vTpg9FYg19J/6FDt35kZlxjzbefk5GWQv3gpjz7xlyc/zdtmJqcWDR6+J/fNq6gsLCA+R+9pNrPfY+M4/5h4wG4o3MvHhv3GhtWLmT5gg/x9gtk/Msf0bhZ7VmN1KjNveRlp7Jv0xxyMq/i6deMe8d8if3/pgGz0i6rvmD4BLWl1/AZ7N0wiz0bPsHFM4h+Iz7F3ad4yrTPYzP5c/1Mtnz7EoacdJzc/Oh4z2TCOte+k5S3HCjARqdh6F167PQaziYYmb8mVzVS8nTR4mhbPBI7eKoQBzsN/TvZ4OygIf6qiflrcsnKLfqWGuBlZV55+MZIB9XjTVucTWpmzX+bXfrjRWxtrXh5UhMcHaw5fCydFyIPq45L+fvY4epcnLQfuNcfgE+j2qj29e6sE6zfkoShwETr5i48cn99nBytSU3L56+j6Yx/+SBp6TW7WMWlXQvCt3xjvh0241UALn69kkNjItD71sMuoHhGJPfcJfbeP46wjyMIemYEeZcSOTzudZI37zTHJKxYj009d5pEPlt0kvJfx9lz31jyr5R8jmRNkmNeahrlNp+RS5cusX//fvr06YODg8OtK9zEb0dzbh1Ux/Robs/Mn/59b8opgzRMnnPrYzN1zaxnHOk28Leabka12/lzD9bqmtZ0M6rdgILYStvXkOdLXj1cFis+Ca60dtQWt315qPr161O/fv3KaIsQQohSyFJ5tTp3bUMhhKiLTDW8WKq2keQlhBAWQEZeapa19lUIIYRARl5CCGERZOSlJslLCCEsgCyVV5PkJYQQFsAkl4dSkeQlhBAWQKYN1SR5CSGEBVBkqbyKrDYUQghhcWTkJYQQFkCmDdUkeQkhhAWQ5KUmyUsIISyAXB5KTY55CSGEBVBMSoW3qpSamspjjz2Gs7Mzrq6ujBkzhqys0n8xIjU1lWeeeYamTZtiZ2dHgwYNePbZZ0lPTy/X48rISwghLIBSS8/zeuyxx0hISGDz5s0UFBQwevRonnrqKZYtW1Zi/OXLl7l8+TIzZswgLCyM8+fPM378eC5fvswPP/xQ5seV5CWEEKJCjh8/zoYNG9i7dy/t27cHYM6cOdx7773MmDEDPz+/G+q0aNGCH3/80Xy7YcOGvPvuuzz++OMUFhZibV22tCTThkIIYQFq47RhdHQ0rq6u5sQF0KdPH7RaLX/++WeZ95Oeno6zs3OZExfIyEsIISzC7ZykbDAYMBgMqjK9Xo9er7+tNiUmJuLl5aUqs7a2xt3dncTExDLtIzk5menTp/PUU0+V67Fl5CWEEBbAZFIqvEVFReHi4qLaoqKiSn2sV155BY1Gc9PtxIkTt92njIwMBgwYQFhYGG+99Va56srISwghLMDtLNiIiIhgypQpqrKbjbpeeOEFRo0addN9hoSE4OPjw5UrV1TlhYWFpKam4uPjc9P6mZmZ3HPPPTg5ObFq1Sp0Ot3NO3EdSV5CCGEBbufYVXmnCOvVq0e9evVuGRceHk5aWhr79++nXbt2AGzduhWTyUSnTp1KrZeRkUG/fv3Q6/WsWbMGW1vbMrftbzJtKIQQokKaNWvGPffcw5NPPsmePXv4448/mDRpEsOGDTOvNIyPjyc0NJQ9e/YARYmrb9++ZGdns2DBAjIyMkhMTCQxMRGj0Vjmx5aRlxBCWIDaelX5pUuXMmnSJHr37o1Wq+Whhx7i//7v/8z3FxQUEBsbS05ODgAHDhwwr0Rs1KiRal9xcXEEBQWV6XEleQkhhAWordc2dHd3L/WEZICgoCDVr0D37NmzUn4VWpKXEEJYgNp6hY0ao/zL5eXlKZGRkUpeXl5NN6VaSb+l3/8G/9Z+/xtoFKUSxm8WLCMjAxcXF/MZ3v8W0m/p97/Bv7Xf/way2lAIIYTFkeQlhBDC4kjyEkIIYXH+9clLr9cTGRl52xeotDTSb+n3v8G/td//Bv/6BRtCCCEsz79+5CWEEMLySPISQghhcSR5CSGEsDiSvIQQQlicf3Xymjt3LkFBQdja2tKpUyfzJfvrsh07djBw4ED8/PzQaDSsXr26pptU5aKioujQoQNOTk54eXkxePBgYmNja7pZVe7zzz+nVatWODs74+zsTHh4OOvXr6/pZlW7999/H41Gw+TJk2u6KaIS/WuT13fffceUKVOIjIzkwIEDtG7dmn79+t3wq6B1TXZ2Nq1bt2bu3Lk13ZRq89tvvzFx4kR2797N5s2bKSgoMP+eUF1Wv3593n//ffbv38++ffvo1asXgwYN4ujRozXdtGqzd+9e5s+fT6tWrWq6KaKy1eylFWtOx44dlYkTJ5pvG41Gxc/PT4mKiqrBVlUvQFm1alVNN6PaXblyRQGU3377raabUu3c3NyUr776qqabUS0yMzOVxo0bK5s3b1Z69OihPPfcczXdJFGJ/pUjr/z8fPbv30+fPn3MZVqtlj59+hAdHV2DLRPVIT09HSj6HaJ/C6PRyPLly8nOziY8PLymm1MtJk6cyIABA1Sfc1F3/Ct/zys5ORmj0Yi3t7eq3NvbmxMnTtRQq0R1MJlMTJ48ma5du9KiRYuabk6VO3z4MOHh4eTl5eHo6MiqVasICwur6WZVueXLl3PgwAH27t1b000RVeRfmbzEv9fEiRM5cuQIO3furOmmVIumTZsSExNDeno6P/zwAyNHjuS3336r0wns4sWLPPfcc2zevBlbW9uabo6oIv/K5OXp6YmVlRVJSUmq8qSkJHx8fGqoVaKqTZo0iV9++YUdO3ZQv379mm5OtbCxsaFRo0YAtGvXjr179zJ79mzmz59fwy2rOvv37+fKlSu0bdvWXGY0GtmxYweffvopBoMBKyurGmyhqAz/ymNeNjY2tGvXji1btpjLTCYTW7Zs+dccD/g3URSFSZMmsWrVKrZu3UpwcHBNN6nGmEwmDAZDTTejSvXu3ZvDhw8TExNj3tq3b89jjz1GTEyMJK464l858gKYMmUKI0eOpH379nTs2JFZs2aRnZ3N6NGja7ppVSorK4vTp0+bb8fFxRETE4O7uzsNGjSowZZVnYkTJ7Js2TJ++uknnJycSExMBMDFxQU7O7sabl3ViYiIoH///jRo0IDMzEyWLVvG9u3b2bhxY003rUo5OTndcDzTwcEBDw+Pf8Vxzn+Nml7uWJPmzJmjNGjQQLGxsVE6duyo7N69u6abVOW2bdumADdsI0eOrOmmVZmS+gsoixYtqummVan//Oc/SmBgoGJjY6PUq1dP6d27t7Jp06aablaNkKXydY/8JIoQQgiL86885iWEEMKySfISQghhcSR5CSGEsDiSvIQQQlgcSV5CCCEsjiQvIYQQFkeSlxBCCIsjyUsIIYTFkeQlhBDC4kjyEkIIYXEkeQkhhLA4kryEEEJYnP8Hubu9N6WKz1sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 500x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Y = walmart_data['Weekly_Sales']\n",
    "walmart_data = walmart_data.drop(['Holiday_Flag'],axis=1)\n",
    "X = walmart_data.drop(['Weekly_Sales'],axis=1)\n",
    "Train_X, Test_X, Train_Y, Test_Y = train_test_split(X, Y, train_size=0.8, test_size=0.2, random_state=100)\n",
    "\n",
    "std = StandardScaler()\n",
    "Train_X_std = std.fit_transform(Train_X)\n",
    "Test_X_std = std.transform(Test_X)\n",
    "\n",
    "correlation_matrix = np.corrcoef(Train_X_std, rowvar=False)\n",
    "\n",
    "plt.figure(figsize=(5, 4))\n",
    "\n",
    "# 绘制热力图\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt='.2f', linewidths=0.5)\n",
    "\n",
    "# 添加标题\n",
    "plt.title('Correlation Matrix')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回归模型的截距为  1034652.5608208469\n",
      "回归模型的截距为  1034652.5608208469\n",
      "回归模型截距为  1034652.5608208469\n"
     ]
    }
   ],
   "source": [
    "MLR = LinearRegression().fit(Train_X_std,Train_Y)\n",
    "pred1 = MLR.predict(Train_X_std)\n",
    "print('回归模型的截距为 ',MLR.intercept_)\n",
    "\n",
    "RLR = Ridge().fit(Train_X_std,Train_Y)\n",
    "pred1 = RLR.predict(Train_X_std)\n",
    "print('回归模型的截距为 ',RLR.intercept_)\n",
    "\n",
    "LLR = Lasso().fit(Train_X_std,Train_Y)\n",
    "pred1 = LLR.predict(Train_X_std)\n",
    "print('回归模型截距为 ',LLR.intercept_)\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fb",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
